Applied Scientist Intern

Skills

About the Role

You will own an end-to-end machine learning project, from data exploration and feature engineering through training, benchmarking, deployment, and monitoring. You will apply LLMs, deep learning, gradient boosting, causal inference, A/B testing, and statistical methods to solve problems in credit, fraud, growth, or spend management. You will collaborate with product and business stakeholders to turn models and insights into user-facing features and strategy.

Requirements

• Currently pursue a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or another quantitative field with an expected graduation date between December 2027 and 2029
• Understand machine learning, statistics, probability, and optimization
• Have interest or experience integrating LLMs and agents into applied solutions
• Use Python and data science libraries including pandas, scikit-learn, NumPy, and PyTorch
• Use SQL to wrangle data in a modern data warehouse
• Have experience curating datasets and building and evaluating machine learning models
• Communicate complex concepts to technical and non-technical audiences

Responsibilities

• Own the machine learning lifecycle from data exploration and feature engineering through training, benchmarking, deployment, and monitoring
• Leverage large language models to solve novel problems and create product capabilities
• Apply machine learning techniques including deep learning, gradient boosting, and causal inference
• Quantify impact through A/B tests and statistical methods
• Partner with product and business leaders to translate models and insights into strategy and user-facing features

Benefits

• Apple MacBook
• Catered lunches in the NYC office Monday through Friday
• Weekly coffee stipend
• Housing stipend